Validation of the online Toddler Autism and Development Adaptive Screener (TADAS) at 18 months

18 个月大时在线幼儿自闭症和发育适应性筛查 (TADAS) 的验证

• 批准号: 10662589
• 负责人: Barbara Jo Howard
• 金额: $ 85.41万
• 依托单位: TOTAL CHILD HEALTH, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10662589
• 关键词: Academy; Advisory Committees; Age; Algorithms; American; Autism Diagnosis; Certification; Characteristics; Child; Childhood; Chiroptera; Clinical; Collaborations; Communities; Computer software; Computers; Consent; Data; Development; Developmental Delay Disorders; Diagnosis; Diagnostic tests; Early Intervention; Early identification; Equipment and supply inventories; Gold; Grant; Insurance Carriers; Interview; Language; Language Tests; Machine Learning; Medicaid services; Municipalities; Outcome; Parents; Patient Education; Patients; Pediatrics; Performance; Phase; Physicians; Population; Preventive; Preventive service; Primary Health Care; Process; Psychometrics; Qi; Questionnaires; Recommendation; Research; Risk; Sampling; Screening procedure; Series; Side; Socioeconomic Status; Specialty Boards; Specificity; Support System; Testing; Time; Toddler; Validation; Visit; Vocabulary; Well Child Visits; Work; autism spectrum disorder; autistic children; base; care coordination; care systems; case-based; cost; data repository; data sharing; demographics; developmental disease; early screening; evidence base; follow-up; improved; improved outcome; innovation; language impairment; learning strategy; machine learning algorithm; novel; online delivery; programs; racial and ethnic; recruit; response; screening; service intervention; study population; theories; tool

Project Summary Abstract The American Academy of Pediatrics has mandated identification of autism (ASD) beginning at the routine 18-month visit, based on evidence that early intervention for children with autism improves outcomes. However, the US Preventive Task Force has not endorsed that recommendation, in part because of limited validity data. This study builds on a series of studies by our group aimed at improving early autism identification. We have developed a promising novel and efficient screener for primary care called Toddler Autism and Development Adaptive Screen (TADAS). The TADAS was developed using machine learning to automatically determine which items to collect from the parent in each case based on data from a community sample in a previous project. Currently used autism screeners have high rates of missing children who have signs of ASD or Developmental Delay (DD) and also problematic and costly rates of over-referral. Data from our prior studies showed that TADAS had nearly three times the sensitivity (.94 vs .32) and similar specificity (.87 vs .90) to the currently recommended and most popular screen for autism (M-CHAT-R/F) and more than twice the sensitivity (.94 vs .39) and similar specificity (.79 vs .85) for developmental delay of the current standard Age & Stages Questionnaire-3tm. The TADAS also provides greater efficiencies since a difficult-to-implement recommended follow-up interview is not required and there is less parent burden in overall number of items. This proposal is to program TADAS for online delivery then validate it against diagnostic testing for ASD and DD in a new community sample of children coming for their 18-month well child visit that better reflects the U.S. population in racial, ethnic and socioeconomic status than our original study population. If our initial results are further validated in this study, screening for early ASD and DD could be much more effective with corresponding improvements in child outcomes through earlier intervention. A side benefit of the project will be an efficient Computer Adaptive Testing language screen based on existing national language abilities data that could be used independently to both track language impaired toddlers who are at risk for later development of autism as well as for general language screening in primary care. These innovative new tools are being created within an online clinical support system, called CHADIS that has additional features to assist in the entire process of early identification, referral, and tracking. CHADIS provides individualized patient education for the parent; clinician use of care coordination software connecting primary care with early intervention services with options to share data and track outcomes; as well as options for doctors to receive required Board re-certification credits for participating in autism screening quality improvement efforts. The final product and “system of care” of value to clinicians, insurers, municipalities and states.

项目摘要 美国儿科学会已经授权从10岁开始识别自闭症（ASD）。 常规18个月的访问，基于自闭症儿童早期干预改善的证据 结果。然而，美国预防工作队并没有认可这一建议，部分原因是 因为有效性数据有限。这项研究建立在我们小组的一系列研究的基础上， 改善自闭症的早期识别。我们开发了一种有前途的新型高效筛选剂， 初级保健称为幼儿自闭症和发育自适应筛查（TADAS）。TADAS是 使用机器学习开发，自动确定从父母那里收集哪些物品 每一个案例都是基于以前项目中社区样本的数据。目前使用的自闭症 筛查人员发现有ASD或发育迟缓迹象的儿童失踪率很高， 也有问题和昂贵的过度转诊率。我们之前的研究数据显示，TADAS 其灵敏度（0.94 vs 0.32）和特异性（0.87 vs 0.90）几乎是目前的三倍。 推荐和最受欢迎的自闭症筛查（M-CHAT-R/F）和两倍以上的灵敏度 (.94与当前标准年龄相比， 阶段问卷-3 tm。TADAS还提供了更高的效率，因为难以实施 不需要进行建议的后续面谈，并且在总体数量上减少了父母的负担 项目.该提案旨在对TADAS进行在线交付编程，然后根据诊断测试对其进行验证 在一个新的社区样本中， 比我们最初的研究更好地反映了美国人口的种族，民族和社会经济地位 人口如果我们的初步结果在这项研究中得到进一步验证，筛查早期ASD和DD 可以更有效地与相应的改善儿童的成果，通过早期 干预该项目的一个附带好处将是一个有效的计算机自适应测试语言 根据现有的国家语言能力数据进行筛选，这些数据可以独立用于跟踪 有语言障碍的幼儿，他们有患自闭症的风险， 初级保健中的语言筛查。这些创新的新工具正在创建一个在线 临床支持系统，称为CHADIS，具有额外的功能，以协助整个过程， 早期识别、转诊和跟踪。CHADIS提供个性化的患者教育， 父母;临床医生使用护理协调软件将初级护理与早期干预联系起来 提供共享数据和跟踪结果的选项的服务;以及医生接收 参与自闭症筛查质量改进所需的委员会重新认证学分 努力最终产品和“护理系统”对临床医生、保险公司、市政当局和州都有价值。